Machine Learning
| Title | Μηχανική Μάθηση / Machine Learning |
| Code | DWS101 |
| Faculty | Sciences |
| School | Informatics |
| Cycle / Level | 2nd / Postgraduate |
| Teaching Period | Winter |
| Coordinator | Ioannis Vlachavas |
| Common | Yes |
| Status | Active |
| Course ID | 600015628 |
Programme of Study: PMS EPISTĪMĪ DEDOMENŌN KAI PAGKOSMIOU ISTOU (2018 éōs sīmera) PF
Registered students: 21
| Orientation | Attendance Type | Semester | Year | ECTS |
|---|---|---|---|---|
| KORMOS | Elective Courses belonging to the selected specialization | 1 | 1 | 7.5 |
| Academic Year | 2019 – 2020 |
| Class Period | Winter |
| Faculty Instructors |
|
| Weekly Hours | 3 |
| Total Hours | 39 |
| Class ID | 600153645
|
Course Type 2016-2020
- Scientific Area
- Skills Development
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
- Face to face
Digital Course Content
- e-Study Guide https://qa.auth.gr/en/class/1/600153645
- eLearning (Moodle): https://elearning.auth.gr/course/view.php?id=8135
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
- Greek (Instruction, Examination)
- English (Examination)
Learning Outcomes
Students become familiar with the main concepts as well as with specific data analysis and machine learning techniques and become also familiar with many applications of it. They also acquire skills in reviewing scientific papers, giving scientific presentations and working in practice with data using various algorithms and relevant machine learning software.
General Competences
- Apply knowledge in practice
- Retrieve, analyse and synthesise data and information, with the use of necessary technologies
- Adapt to new situations
- Make decisions
- Work autonomously
- Work in teams
- Advance free, creative and causative thinking
Course Content (Syllabus)
Introduction, Regression, Decision Trees, Rule Learning, Instance - Based Learning, Bayesian Learning, Learning with Genetic Algorithms, Model Evaluation, Clustering, Association Rules, Feature Selection and Discretization, Ensemble Methods, Reinforcement Learning, Text Mining, Machine Learning Software (Python).
Keywords
Machine Learning, Supervised learning, Unsupervised Learning, Classification, Regression
Educational Material Types
- Notes
- Slide presentations
Use of Information and Communication Technologies
Use of ICT
- Use of ICT in Course Teaching
- Use of ICT in Communication with Students
- Use of ICT in Student Assessment
Description
Slides in electronic format, machine learning software
Course Organization
| Activities | Workload | ECTS | Individual | Teamwork | Erasmus |
|---|---|---|---|---|---|
| Lectures | 39 | ✓ | |||
| Reading Assigment | 42 | ✓ | |||
| Project | 42 | ✓ | |||
| Written assigments | 39 | ✓ | |||
| self-study | 63 | ✓ | |||
| Total | 225 |
Student Assessment
Description
10% review paper and presenation of a machine leanring application area, 20% practical projects on analyzing data using software (Python), 70% written exams. These criteria are clearly stated at the course's web page.
Student Assessment methods
- Written Exam with Multiple Choice Questions (Summative)
- Written Exam with Short Answer Questions (Summative)
- Written Exam with Extended Answer Questions (Summative)
- Written Assignment (Summative)
- Performance / Staging (Summative)
- Written Exam with Problem Solving (Summative)
- Labortatory Assignment (Summative)
Bibliography
Course Bibliography (Eudoxus)
Διδακτικές Σημειώσεις
Additional bibliography for study
- Machine Learning, T. Mitchell, McGraw Hill, 1997.
- The Elements of Statistical Learning:Data Mining, Inference, and Prediction, T. Hastie, R. Tibshirani and J. Friedman, Springer, 2nd edition, 2009.
- Machine Learning:The Art and Science of Algorithms that Make Sense of Data, Peter Flach, Cambridge University Press, 2012.
- Introduction to Machine Learning with Python: A Guide for Data Scientists, Sarah Guido and Andreas C. Müller, O' Reilly Media, 2016.
- Data Mining, Practical Machine Learning Tools and Techniques with Java Implementation (second edition), Ian Witten & Eibe Frank, Morgan Kaufmann, 2005.
- Introduction to Machine Learning (Draft of incomplete Notes), Nils J. Nilsson, 2015 (https://ai.stanford.edu/~nilsson/mlbook.html)
- Introduction to Machine Learning, Ethem Alpaydin, The MIT Press, October 2004.
- Introduction to Data Mining. Pang-Ning Tan, Michael Steinbach and Vipin Kumar. Pearson Addison Wesley. 2005
Last Update
30-01-2020
